Abstract

In current optical communication networks, it is becoming increasingly important to use optical performance monitoring (OPM), which monitors the transmission characteristics of optical signals, to detect early degradation of features that may affect communication quality. This paper describes a method to realize OPM by training a deep neural network (DNN) with data obtained from digital coherent transceivers. In addition, we propose a dataset generation method, Data Augmentation using Differential Image (DADI), to enable the DNN to train with fewer data. We demonstrate that this method simultaneously classifies the optical signal to noise ratio and polarization mode dispersion with high accuracy. Furthermore, we apply DNN-based OPM to detect and localize soft failure in a multi-span environment. We develop a method to identify the location of soft failure by collecting information from DNN-based OPM estimated at multiple points in multi-span optical transmission links. We demonstrate that the method correctly localizes the soft failure with more than 90% accuracy on average (min, 85%; max, 98%) under single-channel experimental conditions.

© 2022 Optica Publishing Group

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